Search Results for author:
Found 21 papers, 7 papers with code
Kernels on Attributed Pointsets with Applications
This paper introduces kernels on attributed pointsets, which are sets of vectors embedded in an euclidean space.
Distributed Weighted Parameter Averaging for SVM Training on Big Data
Experimental results on a variety of toy and real world datasets show that our approach is significantly more accurate than parameter averaging for high number of partitions.
A Methodology for Customizing Clinical Tests for Esophageal Cancer based on Patient Preferences
Our objective is to devise a general methodology for customizing tests using user preferences so that expensive or uncomfortable tests can be avoided.
SPIN: A Fast and Scalable Matrix Inversion Method in Apache Spark
The growth of big data in domains such as Earth Sciences, Social Networks, Physical Sciences, etc.
Distributed, Parallel, and Cluster Computing
Map Enhanced Route Travel Time Prediction using Deep Neural Networks
Travel time estimation is a fundamental problem in transportation science with extensive literature.
Read what you need: Controllable Aspect-based Opinion Summarization of Tourist Reviews
Manually extracting relevant aspects and opinions from large volumes of user-generated text is a time-consuming process.
Scalable Backdoor Detection in Neural Networks
Recently, it has been shown that deep learning models are vulnerable to Trojan attacks, where an attacker can install a backdoor during training time to make the resultant model misidentify samples contaminated with a small trigger patch.
TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving
Training vision-based Autonomous driving models is a challenging problem with enormous practical implications.
Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach
The networked opinion diffusion in online social networks (OSN) is often governed by the two genres of opinions - endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news, feeds etc.
Convex Online Video Frame Subset Selection using Multiple Criteria for Data Efficient Autonomous Driving
We design a novel convex optimization-based multi-criteria online subset selection algorithm that uses a thresholded concave function of selection variables.
Finding High-Value Training Data Subset through Differentiable Convex Programming
In this paper, we study the problem of selecting high-value subsets of training data.
AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal Explanations
We propose AR-BERT, a novel two-level global-local entity embedding scheme that allows efficient joint training of KG-based aspect embeddings and ALSC models.
Aspect-Based Sentiment Analysis (ABSA)
Explanation Generation
+3
PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment.
MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs
Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace.
Offsetting Unequal Competition through RL-assisted Incentive Schemes
On inspecting, we realize that an overall incentive scheme for the weak team does not incentivize the weaker agents within that team to learn and improve.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
CheckSel: Efficient and Accurate Data-valuation Through Online Checkpoint Selection
Another key contribution is the study of data valuation in the domain adaptation setting, where a data value estimator obtained using checkpoints from training trajectory in the source domain training dataset is used for data valuation in a target domain training dataset.
Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes
In this work, we provide a novel unsupervised model and inference method for learning MTPP in presence of event sequences with missing events.
LearnDefend: Learning to Defend against Targeted Model-Poisoning Attacks on Federated Learning
We also show that LearnDefend is robust to size and noise in the marking of clean examples in the defense dataset.
In-Context Ability Transfer for Question Decomposition in Complex QA
We transfer the ability to decompose complex questions to simpler questions or generate step-by-step rationales to LLMs, by careful selection from available data sources of related tasks.
VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AI
In this work, we propose a controllable framework for data-centric trustworthy AI (DCTAI)- VTruST, that allows users to control the trade-offs between the different trustworthiness metrics of the constructed training datasets.
Learning Cross-Task Attribute - Attribute Similarity for Multi-task Attribute-Value Extraction
We perform detailed experiments to show that our method indeed increases the macro-F1 scores for attribute value extraction in general, and for labels with low training data in particular.
